Why isn't medical imaging improving faster with AI?
AI in healthcare is already playing a big role in diagnosing the diseases, assisting patients or helping the medical staff in supplying various things or performing actions.
The medical imaging helps AI to diagnosis the diseases without help radiologist. And to develop the AI model using medical images to diagnosis various disease need training datasets for machine learning algorithm helping to detect with accuracy.
But just like AI, medical imaging is not improving faster making sometimes difficult or more time taking to diagnosis the diseases. Actually, while developing such highly sensitive models, high-quality training data is required and there is lack of such annotated data to train the models.
The more training data is feed into the model, it will learn the detection process with more accuracy resulting predicting the faster diagnosis. And to improve the quality and quantity of data, more dedicated and fast labeling process is required.
Hence, data annotation companies now use the AI-assisted labeling process to annotate the medical images at faster speed with better accuracy.  Compare to manual annotation, AI companies can get multiple times faster annotated data for machine learning algorithms.
Once the training data will start available in large quantity for medical imaging analysis process through AI will also improve. The AI-assisted data annotation process can only help to produce the medical imaging datasets in large quantity for better predictions in healthcare sector.